Embodiments relate to apparatuses and methods configured for automatic learning and/or optimizing an order of items appearing in a sequence. Particular embodiments employ an engine to recognize sequences (e.g., lists of items) repeatedly encountered by a user. Examples of such sequences can include grocery lists, and emails present in an in-box. The engine then references available metadata associated with the sequence and its items, in order to present the user with an optimized sequence tailored to one or more criteria. Examples of available metadata can include sensed location information (of the user and/or other entities), temporal information, contextual influences, historical actions by the user, and/or general population habits (e.g., as may be determined via crowdsourcing). Certain embodiments may further generate a modified sequence based upon suggestions afforded by metadata associated with the sequence. Embodiments may utilize a self-learning scoring algorithm to perform sequence recognition, optimization, and/or modification.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A computer-implemented method comprising: an in-memory database engine receiving a first sequence comprising a plurality of items in a first order, one or more of the items having an attribute; the in-memory database engine storing the first sequence in an in-memory database remote from a mobile device; the in-memory database engine recognizing the first sequence as a list of products for purchase in a facility; the in-memory database engine identifying available first metadata including crowdsourced data relevant to the first sequence; the in-memory database engine identifying available second metadata including internet data of an alternative product; the in-memory database engine identifying available third metadata including internet data indicating an arrangement comprising an aisle location and a shelf location of the products and the alternative product in the facility; the in-memory database engine storing the available first, second, and third metadata in the in-memory database; the in-memory database engine processing the first sequence and the available first second, and third metadata according to a criterion of the attribute, to generate a second sequence, wherein the processing comprises the in-memory database engine, performing a matching process with the crowdsourced data according to a dictionary located external to the in-memory database, to recognize the first sequence as a type previously encountered by the user, and changing the first order according to the criterion determined by the type; the in-memory database storing the second sequence as a data object in the in-memory database; and the in-memory database engine communicating the second sequence including the aisle location and the shelf location in the facility of each of the products and the alternative product to a user of the mobile device for caching.
2. The method as in claim 1 further comprising: the in-memory database engine processing the second sequence according to a suggestion to generate a third sequence; and the in-memory database engine communicating the third sequence to the user.
3. The method as in claim 1 wherein the available metadata further comprises behavior of the user.
4. The method as in claim 1 wherein the available metadata further comprises sensor data.
5. The method as in claim 1 wherein the processing is according to a self-learning scoring algorithm based upon information from the dictionary.
6. The method as in claim 1 wherein the second sequence comprises the items of the first sequence in a second order different from the first order.
7. The method as in claim 1 wherein the second sequence comprises an item different from the first sequence.
8. A non-transitory computer readable storage medium embodying a computer program for performing a method, said method comprising: an in-memory database engine receiving a first sequence comprising a plurality of items in a first order, one or more of the items having an attribute; the in-memory database engine storing the first sequence in an in-memory database remote from a mobile device; the in-memory database engine recognizing the first sequence as a list of products for purchase in a facility; the in-memory database engine processing the first sequence to recognize a sequence type; the in-memory database engine identifying available first metadata relevant to the first sequence; the in-memory database engine identifying available second metadata including internet data of an alternative product; the in-memory database engine identifying available third metadata including internet data indicating an arrangement comprising an aisle location and a shelf location of the products and the alternative product in the facility; the in-memory database engine storing the available first, second, and third metadata in the in-memory database; the in-memory database engine processing the first sequence and the available first second, and third metadata according to a criterion of the attribute determined from the sequence type, to generate a second sequence, wherein the processing comprises the in-memory database engine, performing a matching process with the crowdsourced data according to a dictionary located external to the in-memory database, to recognize the first sequence as a type previously encountered by the user, and changing the first order according to the criterion determined by the type; the in-memory database storing the second sequence as a data object in the in-memory database; and the in-memory database engine communicating the second sequence including the aisle location and the shelf location in the facility of each of the products and the alternative product to a user of the mobile device for caching, wherein the second sequence comprises an item different from the first sequence.
9. The non-transitory computer readable storage medium as in claim 8 wherein the method further comprises: the in-memory database engine processing the second sequence according to a suggestion to generate a third sequence; and the in-memory database engine communicating the third sequence to the user.
10. The non-transitory computer readable storage medium as in claim 8 wherein the available metadata further comprises behavior of the user.
11. The non-transitory computer readable storage medium as in claim 8 wherein the available metadata further further comprises sensor data.
12. The non-transitory computer readable storage medium as in claim 8 wherein the in-memory database engine recognizes the sequence type utilizing a self-learning scoring algorithm based upon information from the dictionary.
13. A computer system comprising: one or more processors; a software program, executable on said computer system, the software program configured to cause an in-memory database engine remote from a mobile device to: receive a first sequence comprising a plurality of items in a first order, one or more of the items having an attribute; store the first sequence in an in-memory database; recognize the first sequence as a list of products for purchase in a facility; identify available first metadata relevant to the first sequence, the available first metadata selected from behavior of the user, crowdsourced data, or sensor data; identify available second metadata including internet data of an alternative product identify available third metadata including internet data indicating an arrangement comprising an aisle location and a shelf location of the products and the alternative product in the facility; store the available first, second, and third metadata in the in-memory database; process the first sequence and the available metadata according to a criterion of the attribute, to generate a second sequence, the in-memory database engine, performing a matching process with the crowdsourced data according to a dictionary located external to the in-memory database, to recognize the first sequence as a type previously encountered by the user, and changing the first order according to the criterion determined by the type; the in-memory database storing the second sequence as a data object in the in-memory database; and communicate the second sequence including the aisle location and the shelf location in the facility of each of the products and the alternative product to a user of the mobile device for caching.
14. The computer system as in claim 13 wherein the software program is further configured to cause the in-memory database engine to, process the second sequence according to a suggestion to generate a third sequence; and communicate the third sequence to the user.
15. The computer system as in claim 13 wherein the processing is according to a self-learning scoring algorithm based upon information from the dictionary.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
February 5, 2015
January 8, 2019
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